Abstract
A general-purpose pattern recognition code, is a modification of the RECOG program, written at Lawrence Livermore National Laboratory. RECOG-ORNL contains techniques for preprocessing, analyzing, and displaying data, and for unsupervised and supervised learning. Data preprocessing routines transform the data into useful representations by autocalling, selecting important variables, and/or adding products or transformations of the variables of the data set. Data analysis routines use correlations to evaluate the data and interrelationships among the data. Display routines plot the multidimensional patterns in two dimensions or plot histograms, patterns, or one variable versus another. Unsupervised learning techniques search for classes contained inherently in the data. Supervised learning techniques use known information about some of the data to generate predicted properties for an unknown set.
- Developers:
- Release Date:
- 2006-05-29
- Project Type:
- Closed Source
- Software Type:
- Scientific
- Programming Languages:
-
NESC9967/01: FORTRAN-IV NESC9967/02: FORTRAN-77
- Sponsoring Org.:
-
USDOE
- Code ID:
- 120853
- Site Accession Number:
- 4343
- Research Org.:
- OECD Nuclear Energy Agency
- Country of Origin:
- United States
Citation Formats
Begovich, C. L., and Larson, N. M.
RECOG-ORNL.
Computer Software.
USDOE.
29 May. 2006.
Web.
doi:10.11578/dc.20240119.27.
Begovich, C. L., & Larson, N. M.
(2006, May 29).
RECOG-ORNL.
[Computer software].
https://doi.org/10.11578/dc.20240119.27.
Begovich, C. L., and Larson, N. M.
"RECOG-ORNL." Computer software.
May 29, 2006.
https://doi.org/10.11578/dc.20240119.27.
@misc{
doecode_120853,
title = {RECOG-ORNL},
author = {Begovich, C. L. and Larson, N. M.},
abstractNote = {A general-purpose pattern recognition code, is a modification of the RECOG program, written at Lawrence Livermore National Laboratory. RECOG-ORNL contains techniques for preprocessing, analyzing, and displaying data, and for unsupervised and supervised learning. Data preprocessing routines transform the data into useful representations by autocalling, selecting important variables, and/or adding products or transformations of the variables of the data set. Data analysis routines use correlations to evaluate the data and interrelationships among the data. Display routines plot the multidimensional patterns in two dimensions or plot histograms, patterns, or one variable versus another. Unsupervised learning techniques search for classes contained inherently in the data. Supervised learning techniques use known information about some of the data to generate predicted properties for an unknown set.},
doi = {10.11578/dc.20240119.27},
url = {https://doi.org/10.11578/dc.20240119.27},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20240119.27}},
year = {2006},
month = {may}
}